Principal Research Engineer, Post-TrainingActive$275K–$400K
The opportunity
About the Role and Team As a Principal Research Engineer on the Post-Training team, you will drive the technical vision, execution, and evolution of the systems that transform foundation models into intelligent, engaging, and aligned products. Specifically, your team focuses on…
What you'll do
Technical Leadership & Mentorship: Define and drive the technical roadmap for mid- and post-training systems, balancing research innovation with production reliability and scalability. You will mentor and grow a team of researchers and engineers through technical guidance, design reviews, and career development. Establish best practices for experimentation, model development, and deployment.
Research & Model Development: Lead the development of alignment algorithms, optimization techniques, and training objectives to improve model capabilities and data efficiency. Drive advances in mid- and post-training methodologies including reinforcement learning, preference optimization, supervised fine-tuning, and emerging alignment approaches. Identify and execute high-impact research opportunities that improve model behavior, safety, and user engagement. Develop robust evaluation frameworks and quality signals to measure real-world model performance.
Systems & Infrastructure: Lead the design of efficient training and inference systems for large-scale generative models. Architect scalable data pipelines that transform diverse data sources into high-quality training datasets. Partner with infrastructure teams to optimize distributed training, GPU utilization, and serving efficiency. Drive improvements in experimentation platforms, data quality systems, and model observability.
PhD in Computer Science, Machine Learning, AI, or a related field, or equivalent industry experience.
Significant experience leading technical projects or teams in machine: learning, AI research, or large-scale distributed systems. Experience scaling and mentoring high-performing research and engineering teams.
Deep understanding of modern machine learning techniques, including: transformers, reinforcement learning, alignment methods, and large language models.
What they're looking for
- Strong track record of delivering impactful research or applied ML systems in production environments.
- Expertise in designing, building, and maintaining production-quality ML systems and infrastructure.
- Experience training, serving, debugging, and optimizing large-scale models on GPU-based systems.
- Experience leading teams working on large language model training, mid-training, or post-training.
